Wavelet-Based Noise Removal from Raman Signal to Study PLD Coated Forsterite–Hydroxyapatite Thin Film on Stainless Steel 316l Substrate

Abstract

Raman spectroscopy is proposed here for the study of forsterite–hydroxyapatite (FS–HA) composite coating on a stainless-steel substrate. However, in order to analyze the Raman spectrum accurately, noise and background removal is always required. A comparative study has been done for the correction of background. The waveletbased denoising of the signal was done using level 6 decomposition with sym4 wavelet and the thresholding method used was soft thresholding. In the present work, the effectiveness of the wavelet-based denoising method has been compared with Savitsky–Golay smoothing, quadratic regression, and the low-pass filter method. It is found that the wavelet-based denoising method works better as compared to other methods as it is able to smooth the signal and to increase the SNR while maintaining the peak intensity undistorted. Peaks are calculated for the different composition of the FS–HA composite. The variation of peak location in the processed Raman spectra suggests that the variation in concentration of FS and HA in the coating can be studied by using Raman spectroscopy. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.